The Use of Adaptive Genetic Algorithm for Detecting Kiwifruit’s Variant Subculture Seedling

In order to reduce the possible economic loss brought by variant seedlings in tissue culture, we propose a pattern recognition approach using fitness to dynamically monitor subculture seedlings of kiwifruit based on adaptive Genetic Algorithm. By coding, selection, mutation and crossover the selecte...

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Main Authors: Jun Zeng, Yong Li
Format: Article
Language:English
Published: Bulgarian Academy of Sciences 2017-12-01
Series:International Journal Bioautomation
Subjects:
Online Access:http://www.biomed.bas.bg/bioautomation/2017/vol_21.4/files/21.4_08.pdf
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author Jun Zeng
Yong Li
author_facet Jun Zeng
Yong Li
author_sort Jun Zeng
collection DOAJ
description In order to reduce the possible economic loss brought by variant seedlings in tissue culture, we propose a pattern recognition approach using fitness to dynamically monitor subculture seedlings of kiwifruit based on adaptive Genetic Algorithm. By coding, selection, mutation and crossover the selected primer pairs of the subculture seedlings, we simulate the process of optimizing the kiwifruit’s genomic DNA polymorphism. The result shows that fitness values of kiwifruit’s subculture seedlings can better maintain their genetic stability from the first to the ninth generation in the simulation. But from the tenth generation, the rapid change of the fitness values of subculture seedlings happen. It is in accord with the experimentation, which uses optimized AFLP system for analyzing genetic diversity of 75 samples of seventh to eleventh 5 generations of kiwifruit subculture seedlings.
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spelling doaj.art-cd69bcabdd4a413cb9a3ea46e9bed7282022-12-22T00:04:45ZengBulgarian Academy of SciencesInternational Journal Bioautomation1314-19021314-23212017-12-01214349356The Use of Adaptive Genetic Algorithm for Detecting Kiwifruit’s Variant Subculture SeedlingJun Zeng0Yong LiCollege of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, ChinaIn order to reduce the possible economic loss brought by variant seedlings in tissue culture, we propose a pattern recognition approach using fitness to dynamically monitor subculture seedlings of kiwifruit based on adaptive Genetic Algorithm. By coding, selection, mutation and crossover the selected primer pairs of the subculture seedlings, we simulate the process of optimizing the kiwifruit’s genomic DNA polymorphism. The result shows that fitness values of kiwifruit’s subculture seedlings can better maintain their genetic stability from the first to the ninth generation in the simulation. But from the tenth generation, the rapid change of the fitness values of subculture seedlings happen. It is in accord with the experimentation, which uses optimized AFLP system for analyzing genetic diversity of 75 samples of seventh to eleventh 5 generations of kiwifruit subculture seedlings.http://www.biomed.bas.bg/bioautomation/2017/vol_21.4/files/21.4_08.pdfKiwifruitVariant seedlingTissue cultureAdaptive genetic algorithmFitness function
spellingShingle Jun Zeng
Yong Li
The Use of Adaptive Genetic Algorithm for Detecting Kiwifruit’s Variant Subculture Seedling
International Journal Bioautomation
Kiwifruit
Variant seedling
Tissue culture
Adaptive genetic algorithm
Fitness function
title The Use of Adaptive Genetic Algorithm for Detecting Kiwifruit’s Variant Subculture Seedling
title_full The Use of Adaptive Genetic Algorithm for Detecting Kiwifruit’s Variant Subculture Seedling
title_fullStr The Use of Adaptive Genetic Algorithm for Detecting Kiwifruit’s Variant Subculture Seedling
title_full_unstemmed The Use of Adaptive Genetic Algorithm for Detecting Kiwifruit’s Variant Subculture Seedling
title_short The Use of Adaptive Genetic Algorithm for Detecting Kiwifruit’s Variant Subculture Seedling
title_sort use of adaptive genetic algorithm for detecting kiwifruit s variant subculture seedling
topic Kiwifruit
Variant seedling
Tissue culture
Adaptive genetic algorithm
Fitness function
url http://www.biomed.bas.bg/bioautomation/2017/vol_21.4/files/21.4_08.pdf
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